Les Houches guide to reusable ML models in LHC analyses
Araz, J. Y. 
ORCID: 0000-0001-8721-8042, Buckley, A., Kasieczka, G. , Kieseler, J., Kraml, S., Kvellestad, A., Lessa, A., Procter, T., Raklev, A., Reyes-González, H., Rolbiecki, K., Sekmen, S. & Ünel, G. (2024).
Les Houches guide to reusable ML models in LHC analyses.
SciPost Physics Community Reports,
article number 3.
doi: 10.21468/scipostphyscommrep.3
Abstract
With the increasing usage of machine-learning in high-energy physics analyses, the publication of the trained models in a reusable form has become a crucial question for analysis preservation and reuse. The complexity of these models creates practical issues for both reporting them accurately and for ensuring the stability of their behaviours in different environments and over extended timescales. In this note we discuss the current state of affairs, highlighting specific practical issues and focusing on the most promising technical and strategic approaches to ensure trustworthy analysis-preservation. This material originated from discussions in the LHC Reinterpretation Forum and the 2023 PhysTeV workshop at Les Houches.
| Publication Type: | Article |
|---|---|
| Additional Information: | Copyright J. Y. Araz et al. This work is licensed under the Creative Commons Attribution 4.0 International License. Published by the SciPost Foundation. |
| Subjects: | Q Science > QC Physics |
| Departments: | School of Science & Technology School of Science & Technology > Department of Engineering |
| SWORD Depositor: |
Available under License Creative Commons: Attribution International Public License 4.0.
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